It has become increasingly common to use graphic images to display different types of data. Such displays are easy to interpret and provide the user with an overall picture of a particular data set. One example is our patent specification WO 00/77682 to Compudigm International Limited entitled “Data Visualisation System and Method” in which data values are contoured around a series of points.
It would be particularly desirable to further enhance the graphic representations displayed to a user. One example of enhancement would be to magnify part of but not all of a representation. A floating magnification window could be provided on a computer display which tracks a mouse cursor or other pointing device and magnifies the region of the screen under the cursor. For optimal results, it is preferable to maintain the same size of the overall representation during a magnification process. This will mean that parts of the image will be magnified, parts of the image will remain in that original form, and other parts of the image will be compressed to counteract the effect of magnification.
Non-linear magnification is one technique to achieve magnification in this manner. Non-linear magnification is characterised by enhancing resolution of a particular area or areas of interest, preserving the overall global context of an image, providing magnification within the existing image and not in a separate window, and providing non-occlusion, meaning that magnification of one area does not block out surrounding areas. Algorithms for performing such non-linear magnification are described in Keahey, T. A. “Nonlinear Magnification” Ph.D. Dissertation, Indiana University Computer Science, December 1997, Keahey, T. A. and Robertson, E. L. “Techniques for Non-Linear Magnification Transformations”, Proceedings of the IEEE Symposium on Information Visualisation, IEEE Visualisation, pp 38-45, October 1996 and Keahey, T. A. and Robertson, E. L. “Nonlinear Magnification Fields”, Proceedings of the IEEE Visualisation Conference, IEEE Visualisation, pp 51-58, 1997.
Such algorithms typically require iteration over a computationally expensive series of calculations. It would be particularly advantageous to reduce the number of iterations required by calculating a series of values for a non-linear magnification which are close to the final result acquired by algorithms such as the Keahey algorithm. More efficient iterative techniques would be less computationally expensive than existing methods.